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New Generation of Soil Data in Slovakia – Processing and Application Jaroslava Sobocká Rastislav Skalský Juraj Balkovič Vladimír Hutár Soil Science and.

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Presentation on theme: "New Generation of Soil Data in Slovakia – Processing and Application Jaroslava Sobocká Rastislav Skalský Juraj Balkovič Vladimír Hutár Soil Science and."— Presentation transcript:

1 New Generation of Soil Data in Slovakia – Processing and Application Jaroslava Sobocká Rastislav Skalský Juraj Balkovič Vladimír Hutár Soil Science and Conservation Reseach Institute Department of Soil Science and Mapping

2 Soil/ladscape data for Slovakia: in time line 198019851990199520002005 KPP-DB PEU-DB CMS-PGFZP GCHA DPZ GDPPS KPP-DB – Soil profile database CMS-P – Soil Monitoring database PEU-DB – Pedo-ecological units database GFZP – Regional pedo-geochemical database GCHA – Pedo-geochemical atlas database GDPPS – Geo-referenced database of agricultural soils DPZ – Remote sensing data (auxiliary data)

3 KPP-DB Soil profile database Soil profile location (x,y) about 17 000 soil profiles of agricultural soils Database tables - general soil profile atributes - genetic soil horizons attributes – morphological soil physical and chemical properties Soil profiles distribution within the territory of Slovakia and at regional level R. Skalský

4 CMS-P Soil monitoring database Monitoring sites location (x,y), 318 sites on agricultural soils Database tables – attributes for description of general soil profile properties – attributes for sequence of soil layers – morphological, chemical, physical properties – attributes for soil contamination Monitoring period – provided data in time series (5 year period sampling/recording frequency) Soil profiles distribution within the territory of Slovakia J. Kobza

5 PEU-DB Pedo-ecological units database pedo-ecological units (analogue version) Spatial distribution of topic pedo- ecological units soil-ecological attributes soil production or economic attributes Spatial distribution of regional pedo- ecological unitsaccording to soil-ecological attributes B. Ilavská

6 GFPP Regional pedo-geochemical database spatial distribution of soil mapping units polygons soil profiles localization Tables general attribute data for soil profile soil horizon attribute data for surface and substrate horizon – selected soil physical and chemical properties soil contamination attributes for surface and substrate horizon – 15 risk elements Continous raster models (layers) of soil risk elements content at one-dimensional level pH(H2O) Soil map 1:50 000 J. Sobocká

7 GCHA pedo-geochemical atlas database Soil profile localization (x, y), 5 200 points on both agricultural and forest soils table - attributes for description of general properties of soil profile - soil horizon attribute data for surface and substrate horizon – selected soil physical and chemical properties - soil contamination attributes for surface and substrate horizon – 36 risk elements Publication – analogue interpolated maps of risk elements distribution across the Slovakia J. Čurlík, P. Šefčík

8 DPZ Remote sensing/auxiliary data Digital ortophotomaps: covering all territory of Slovakia, valid for 2002/3, scale: 1:10 000 Satelite images: time series from 1999, covering all territory of Slovakia (LANDSAT, SPOT, IRS) DEM: 30 and 50 m resolution DEM for whole territory of Slovakia Interpretation example: USLE Based Erosion modelling M. Sviček, O. Rybár

9 GDPPS - Geo-referenced database of agricultural soils New-fashioned soil database for Slovakia being built up since 2004 Database representation of General soil survey of agricultural soils of Slovakia (in 1961 – 1970) Modern database enabling application of wide range of pedometrics procedures Examples of analogue inputs R. Skalský

10 GDPPS -Database structure Areal information about soil mapping units distribution Soil profiles localization and attribute data related (same as for KPP-DB), possible number of soil profiles represented: about 200 000 R. Skalský Database aproximation: raster base Interpolated rasters, spatial resolution 250m applied on soil profile data Set of continuous raster layers of soil analytical properties created for discrete depth intervals Measured soil parameters as well as PTF/stationary models derived ones Selected regions of Slovakia

11 GDPPS - Database operability proposal Soil units polygons Average soil profile Expert knowledge based processing rules Average soil attributes R. Skalský

12 What are methods used in digital soil/landscape data processing in Slovakia: a short history 198019851990199520002005 PCA, agglomerative cluster analyses Numerical taxonomy GIS cartography, Expert interpretation Geostatistics Fuzzy k-means Remote sensing data interpretation Static/dynamic soil/landscape modelling

13 First methods and applications 127 soil profile were described by these soil properties (vectors): Texture, soil structure, stoniness, soil consistence, pH in KCl, carbonate content, humus content, CEC, neoformation presence, depth of top horizons, depth of solum Type of data: ranking of qualitative data Type of standardization: standard deviation Similarity coeficient: Manhattan metric Agglomerative strategy: Non-weighted pair-group method Juráň, C.: Numerical ordination os soils on the base of General Survey of Agricultural soils, 1984 Horváthová, J,: Contribution to the Numerical taxonomy method for soil classification,1985 Problems of clusters validation and interpretation J. Sobocká

14 GIS cartography and expert interpretation Polygons as SOTERunit_ID in Slovakia in 1:2.5 million 76 polygons were delineated and described in Slovakia J. Sobocká

15 Soil Degradation in Central and Eastern Europe (SOVEUR) SOTER database formation and application in maps J. Sobocká Various maps producing relating to soil degradation status

16 SSCRI strategy for creation of regional pedo - geochemical maps - location Position location of soil description refer to :global coordinates (WGS 84 – latitude B (degree), longitude L (degree) ) :national grids (S-JTSK – X (meter) Y (meter)) Satellite images Topography maps Orthophotomaps GPS V. Hutár

17 2.49 rmse y 3.49 rmse x 4.29 rms e xy Reference measurement: GPS position accuracy SSRI reference station SAMPLE ACCURACY – refer to the mapping method – with regard to map scale – with regard to sample design V. Hutár cluster regular random SSCRI strategy for creation of regional pedogeochemical maps - Sampling strategy

18 Searching for spatial dependence, analyzing the basic principles in space with regard on accuracy, scale and dimension directional variogram, direction 60 º anisotropic variationdirectional variogram, direction 150º directional variogram, anisotropy 1.6 V. Hutár SSCRI strategy for creation of regional pedo - geochemical maps – geostatistics application

19 Analysing the multivariate objects regarding a.) linear methods (PCA) b.) unimodal methods (CA) Non-hierarical classification of multiobjects using fuzzy k-means alghoritm is used to continuously classify the real-world objects V. Hutár SSCRI strategy for creation of regional pedo - geochemical maps – multivariate analyses, fuzzy k- means

20 A (A1), B and C limit appointed in the Decree no. 531/1994-540 respecting the absolute value respecting the calculated value for non-standard soil linear gradient analysis were used to findings of statistical significance of Cox and silt for heavy metals accumulation V. Hutár SSCRI strategy for creation of regional pedo - geochemical maps Study case 1: Chvojnicka hilly land

21 H.metal BaCoCrCuMoNiVZn A-limit 81539737102 B-limit 2 2 Number of samples with exceeded concentration of risk elements V. Hutár

22 A study case 2: Fuzzy-based digital soil mapping in Považsky Inovec Mt. Point database: Basic inputs: Numeric profile description 90 soil profiles 5 km2 J. Balkovič & G. Čemanová

23 Genetic horizons [cm] Soil stratification Colour: Features of soil genesis: others profile data: Input matrix Scheme of numeric coding of soil properties: J.Balkovič & G. Čemanová

24 Fuzzy k-mean classification (centroids) J. Balkovič & G. Čemanová

25 5A 5B5C 5E5D Interpolated rasters of membership values J. Balkovič & G. Čemanová

26 Digital diffuse soil map obtained by pixel mixture technique Juraj Balkovič & Gabriela Čemanová

27 A study case 3: Digital map of potential water storage in soils (Zahorska lowland) Inputs (source KPP): Sand content [%] Silt content [%] Clay content [%] J. Balkovič, T. Orfánus & R. Skalský

28 Rosetta model for estimation of van Genuchten eq. parameters and validation: SAND, SILT, CLAY ROSETTA PF-curve: Θr, Θs, α, n Regionally defined PTF KPP-DB sandy silt J. Balkovič, T. Orfánus & R. Skalský

29 ΘFWC – field water capacity ΘWP - wilting point h - soil depth [0.5 m] W = 1000 (ΘFWC - ΘWP).h [mm] Potential water storage in soils (up to 50 cm) J. Balkovič, T. Orfánus & R. Skalský


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